Fixed YOLO heads docstrings (#16822)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

tests/test_explorer.py

@@ -1,66 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-
-import PIL
-import pytest
-
-from ultralytics import Explorer
-from ultralytics.utils import ASSETS
-from ultralytics.utils.torch_utils import TORCH_1_13
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(not TORCH_1_13, reason="Explorer requires torch>=1.13")
-def test_similarity():
-    """Test the correctness and response length of similarity calculations and SQL queries in the Explorer."""
-    exp = Explorer(data="coco8.yaml")
-    exp.create_embeddings_table()
-    similar = exp.get_similar(idx=1)
-    assert len(similar) == 4
-    similar = exp.get_similar(img=ASSETS / "bus.jpg")
-    assert len(similar) == 4
-    similar = exp.get_similar(idx=[1, 2], limit=2)
-    assert len(similar) == 2
-    sim_idx = exp.similarity_index()
-    assert len(sim_idx) == 4
-    sql = exp.sql_query("WHERE labels LIKE '%zebra%'")
-    assert len(sql) == 1
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(not TORCH_1_13, reason="Explorer requires torch>=1.13")
-def test_det():
-    """Test detection functionalities and verify embedding table includes bounding boxes."""
-    exp = Explorer(data="coco8.yaml", model="yolo11n.pt")
-    exp.create_embeddings_table(force=True)
-    assert len(exp.table.head()["bboxes"]) > 0
-    similar = exp.get_similar(idx=[1, 2], limit=10)
-    assert len(similar) > 0
-    # This is a loose test, just checks errors not correctness
-    similar = exp.plot_similar(idx=[1, 2], limit=10)
-    assert isinstance(similar, PIL.Image.Image)
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(not TORCH_1_13, reason="Explorer requires torch>=1.13")
-def test_seg():
-    """Test segmentation functionalities and ensure the embedding table includes segmentation masks."""
-    exp = Explorer(data="coco8-seg.yaml", model="yolo11n-seg.pt")
-    exp.create_embeddings_table(force=True)
-    assert len(exp.table.head()["masks"]) > 0
-    similar = exp.get_similar(idx=[1, 2], limit=10)
-    assert len(similar) > 0
-    similar = exp.plot_similar(idx=[1, 2], limit=10)
-    assert isinstance(similar, PIL.Image.Image)
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(not TORCH_1_13, reason="Explorer requires torch>=1.13")
-def test_pose():
-    """Test pose estimation functionality and verify the embedding table includes keypoints."""
-    exp = Explorer(data="coco8-pose.yaml", model="yolo11n-pose.pt")
-    exp.create_embeddings_table(force=True)
-    assert len(exp.table.head()["keypoints"]) > 0
-    similar = exp.get_similar(idx=[1, 2], limit=10)
-    assert len(similar) > 0
-    similar = exp.plot_similar(idx=[1, 2], limit=10)
-    assert isinstance(similar, PIL.Image.Image)

ultralytics/nn/modules/head.py

@@ -19,7 +19,7 @@ __all__ = "Detect", "Segment", "Pose", "Classify", "OBB", "RTDETRDecoder", "v10D
 
 
 class Detect(nn.Module):
-    """YOLOv8 Detect head for detection models."""
+    """YOLO Detect head for detection models."""
 
     dynamic = False  # force grid reconstruction
     export = False  # export mode
@@ -30,7 +30,7 @@ class Detect(nn.Module):
     strides = torch.empty(0)  # init
 
     def __init__(self, nc=80, ch=()):
-        """Initializes the YOLOv8 detection layer with specified number of classes and channels."""
+        """Initializes the YOLO detection layer with specified number of classes and channels."""
         super().__init__()
         self.nc = nc  # number of classes
         self.nl = len(ch)  # number of detection layers
@@ -162,7 +162,7 @@ class Detect(nn.Module):
 
 
 class Segment(Detect):
-    """YOLOv8 Segment head for segmentation models."""
+    """YOLO Segment head for segmentation models."""
 
     def __init__(self, nc=80, nm=32, npr=256, ch=()):
         """Initialize the YOLO model attributes such as the number of masks, prototypes, and the convolution layers."""
@@ -187,7 +187,7 @@ class Segment(Detect):
 
 
 class OBB(Detect):
-    """YOLOv8 OBB detection head for detection with rotation models."""
+    """YOLO OBB detection head for detection with rotation models."""
 
     def __init__(self, nc=80, ne=1, ch=()):
         """Initialize OBB with number of classes `nc` and layer channels `ch`."""
@@ -217,7 +217,7 @@ class OBB(Detect):
 
 
 class Pose(Detect):
-    """YOLOv8 Pose head for keypoints models."""
+    """YOLO Pose head for keypoints models."""
 
     def __init__(self, nc=80, kpt_shape=(17, 3), ch=()):
         """Initialize YOLO network with default parameters and Convolutional Layers."""
@@ -257,10 +257,10 @@ class Pose(Detect):
 
 
 class Classify(nn.Module):
-    """YOLOv8 classification head, i.e. x(b,c1,20,20) to x(b,c2)."""
+    """YOLO classification head, i.e. x(b,c1,20,20) to x(b,c2)."""
 
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
-        """Initializes YOLOv8 classification head to transform input tensor from (b,c1,20,20) to (b,c2) shape."""
+        """Initializes YOLO classification head to transform input tensor from (b,c1,20,20) to (b,c2) shape."""
         super().__init__()
         c_ = 1280  # efficientnet_b0 size
         self.conv = Conv(c1, c_, k, s, p, g)
@@ -277,10 +277,10 @@ class Classify(nn.Module):
 
 
 class WorldDetect(Detect):
-    """Head for integrating YOLOv8 detection models with semantic understanding from text embeddings."""
+    """Head for integrating YOLO detection models with semantic understanding from text embeddings."""
 
     def __init__(self, nc=80, embed=512, with_bn=False, ch=()):
-        """Initialize YOLOv8 detection layer with nc classes and layer channels ch."""
+        """Initialize YOLO detection layer with nc classes and layer channels ch."""
         super().__init__(nc, ch)
         c3 = max(ch[0], min(self.nc, 100))
         self.cv3 = nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, embed, 1)) for x in ch)